I'm trying to implement a code with gnuplot in order to make an animation in 3D to see the evolution of one planet system that I already have its data in a .txt document.
Here's a part of the document. Each column presents the different coordinates of the particles and lines are also coordinates but in different times.
here is an example of the data
I know that I loop like this :
do for [i=0:200]{
plot "Sky".i.".txt" u 1:4
}
but this does not help, because I have all data in different lines, which each one of them presents the positions of the particles in different time.
I would be very thankful if anyone knows how to do that loop but reiterating with lines.
Related
I am trying to create a sphere with lines and dots on it. But since I am plotting the sphere, the lines and the points separately in the code, I don't think Mayavi recognized it as a whole figure. So the lines and points on the rear face of the sphere are also visible. Is there a way to make the whole scene opaque and show only one plane at a time? (Opacity only makes the individual element i.e. sphere or line or point opaque; not the whole figure.)
This is what I am getting now:
This is what I want it to look like. As I rotate this sphere, it should only show the points on the surface that is facing us.
So the software I am using accepts 3D objects in the form of contours or .stl files. The contours I have are along the z-plane(each plane has a unique z). I have had to modify the contours for my experiment and now the contours do not have a unique z for each plane(they are now slightly angled wrt z=0 plane).
The points represent the edges of the 3D object. What would be the best way to take this collection of points and create a .stl file?
I am relatively new to working with python and 3D objects, so any help, pointers or suggestions would be much appreciated.
Edit: I have the simplices and verticies using the Delaunay(), but how do I proceed next?
The co-ordinates of all points are in this text file in the format "x y z".
So after seeking an answer for months and trying to use Meshlab and Blender I finally stumbled across the answer using numpy-stl. Hopeful that it will help others in a similar situation.
Here is the code to generate the .STL file:
from stl import mesh
num_triangles=len(fin_list)
data = np.zeros(num_triangles, dtype=mesh.Mesh.dtype)
for i in range(num_triangles):
#I did not know how to use numpy-arrays in this case. This was the major roadblock
# assign vertex co-ordinates to variables to write into mesh
data["vectors"][i] = np.array([[v1x, v1y, v1z],[v2x, v2y, v2z],[v3x, v3y, v3z]])
m=mesh.Mesh(data)
m.save('filename.stl')
The three vertices that form a triangle in the mesh go in as a vector that define the surface normal. I just collected three such vertices that form a triangle and wrote them into the mesh. Since I had a regular array of points, it was easy to collect the triangles:
for i in range(len(point_list)-1):
plane_a=[]
plane_b=[]
for j in range(len(point_list[i])-1):
tri_a=[]
tri_b=[]
#series a triangles
tri_a.append(point_list[i+1][j])
tri_a.append(point_list[i][j+1])
tri_a.append(point_list[i][j])
#series b triangles
tri_b.append(point_list[i+1][j])
tri_b.append(point_list[i+1][j+1])
tri_b.append(point_list[i][j+1])
#load to plane
plane_a.append(tri_a)
plane_b.append(tri_b)
group_a.append(plane_a)
group_b.append(plane_b)
The rules for choosing triangles for creating a mesh are as follows:
The vertices must be arranged in a counter-clock direction.
Each triangle must share two vertices with adjacent triangles.
The direction normal must point out of the surface.
There were two more rules that I did not follow but it still worked in my case:
1. All coordinates must be positive(In 1st Quadrant only)
2. All triangles must be arranged in an increasing z-order.
Note: There can be two kinds of .STL file formats: Binary and ASCII. numpy-stl writes out in the binary format. More info on STL files can be found here.
Hope this helps!
I have a data set that I receive from an outside source, and have no real control over.
The data, when plotted, shows two clumps of points with several sparse, irrelevant points. Here is a sample plot:
There is a clump of points on the left, clustered around (1, 16). This clump is actually part of a set of points that lies on (or near to) a line stretching from (1, 17.5) to (2.4, 13).
There is also an apparent curve from (1.75, 18) to (2.75, 12.5).
Finally, there are some sparse points above the second curve, around (2.5, 17).
Visually, it's not difficult to separate these groups of points. However, I need to separate these points within the data file into three groups, which I'll call Line, Curve, and Other (the Curve group is the one I actually need). I'd like to write a program that can do this reasonably well without needing to visually see the plot.
Now, I'm going to add a couple items that make this much worse. This is only a sample set of data. While the shapes of the curve and line are relatively constant from one data set to the next, the positions are not. These regions can (and do) shift, both horizontally and vertically. The only real constant is that there's a negative-slope line from the top-left to the bottom-right of the plot, an almost curve from the top-center to the bottom-right, and most of the sparse points are in the top-right corner, above the curve.
I'm on Linux, and I'm out of ideas. I can tell you the approaches that I've tried, though they have not done well.
First, I cleaned up the data set and sorted it in ascending order by x-coordinate. I thought that maybe the points were sorted in some sort of a logical way that would allow me to 'head' or 'tail' the data to achieve the desired result, but this was not the case.
I can write a code in anything (Python, Fortran, C, etc.) that removes a point if it's not within X distance of the previous point. This would be just fine, except that the scattering of the points is such that two points very near each other in x, are separated by an appreciable distance in y. It also doesn't help that the Line and Curve draw near one another for larger x-values.
I can fit a curve to a partial data set. When I sort the data by x-coordinate, for example, I can choose to only plot the first 30 points, or the last 200, or some set of 40 in the middle somewhere. That's not a problem. But the Line points tuck underneath the Curve points, which causes a problem.
If the Line points were fairly constant (which they're not), I could rotate my plot by some angle so that the Line is vertical and I can just look at the points to the right of that line, then rotate back. This may the best way to go about doing this, but in order to do that, I need to be able to isolate the linear points, which is more or less the essence of the problem.
The other idea that seems plausible to me, is to try to identify point density and split the data into separate files by those parameters. I think this is the best candidate for this problem, since it is independent of point location. However, I'm not sure how to go about doing this, especially because the Line and Curve do come quite close together for larger x-values (In the sample plot, it's x-values greater than about 2).
I know this does not exactly fall in with the request of a MWE, but I don't know how I'd go about providing a more classical MWE. If there's something else I can provide that would help, please ask. Thank you in advance.
I need to be able to turn a black and white image into series of lines (start, end points) and circles (start point, radius). I have a "pen width" that's constant.
(I'm working with a screen that can only work with this kind of graphics).
Problem is, I don't want to over complicate things - I could represent any image with loads of small lines, but it would take a lot of time to draw, so I basically want to "approximate" the image using those lines and circles.
I've tried several approaches (guessing lines, working area by area, etc) but none had any reasonable results without using a lot of lines and circles.
Any idea on how to approach this problem?
Thanks in advance!
You don't specify what language you are working in here but I'd suggest OpenCV if possible. If not, then most decent CV libraries ought to support the features that I'm about to describe here.
You don't say if the input is already composed of simple shapes ( lines and polygons) or not. Assuming that it's not, i.e. it's a photo or frame from a video for example, you'll need to do some edge extraction to find the lines that you are going to model. Use a Canny or other edge detector to convert the image into a series of lines.
I suggest that you then extract Circles as they are the richest feature that you can model directly. You should consider using a Hough Circle transform to locate circles in your edge image. Once you've located them you need to remove them from the edge image (to avoid duplicating them in the line processing section below).
Now, for each pixel in the edge image that's 'on' you want to find the longest line segment that it's a part of. There are a number of algorithms for doing this, simplest would be Probabilistic Hough Transform (also available in openCV) to extract line segments which will give you control over the minimum length, allowed gaps etc. You may also want to examine alternatives like LSWMS which has OpenCV source code freely available.
Once you have extracted the lines and circles you can plot them into a new image or save the coordinates for your output device.
Back story: I'm creating a Three.js based 3D graphing library. Similar to sigma.js, but 3D. It's called graphosaurus and the source can be found here. I'm using Three.js and using a single particle representing a single node in the graph.
This was the first task I had to deal with: given an arbitrary set of points (that each contain X,Y,Z coordinates), determine the optimal camera position (X,Y,Z) that can view all the points in the graph.
My initial solution (which we'll call Solution 1) involved calculating the bounding sphere of all the points and then scale the sphere to be a sphere of radius 5 around the point 0,0,0. Since the points will be guaranteed to always fall in that area, I can set a static position for the camera (assuming the FOV is static) and the data will always be visible. This works well, but it either requires changing the point coordinates the user specified, or duplicating all the points, neither of which are great.
My new solution (which we'll call Solution 2) involves not touching the coordinates of the inputted data, but instead just positioning the camera to match the data. I encountered a problem with this solution. For some reason, when dealing with really large data, the particles seem to flicker when positioned in front/behind of other particles.
Here are examples of both solutions. Make sure to move the graph around to see the effects:
Solution 1
Solution 2
You can see the diff for the code here
Let me know if you have any insight on how to get rid of the flickering. Thanks!
It turns out that my near value for the camera was too low and the far value was too high, resulting in "z-fighting". By narrowing these values on my dataset, the problem went away. Since my dataset is user dependent, I need to determine an algorithm to generate these values dynamically.
I noticed that in the sol#2 the flickering only occurs when the camera is moving. One possible reason can be that, when the camera position is changing rapidly, different transforms get applied to different particles. So if a camera moves from X to X + DELTAX during a time step, one set of particles get the camera transform for X while the others get the transform for X + DELTAX.
If you separate your rendering from the user interaction, that should fix the issue, assuming this is the issue. That means that you should apply the same transform to all the particles and the edges connecting them, by locking (not updating ) the transform matrix until the rendering loop is done.